What is a notebook

• The standard for programming in python is the .py file which can hold a block of code which can contain lines of code that allow you to export the results as visualisations or data files.
• Jupyter Notebooks have been developed with the data science and analytical community.
• Notebooks are a collection interactive cells which a user can run as a collection or individually, based on the current state of program.
• Cells can be denoted as Code, Markdown or Raw Depending on use case.
  • Code cells use a process called a kernel to run programme elements in the user selected code base (e.g. Python or R).
  • Markdown cells allow the user to include formatted text and other elements (such as links and images).
  • Raw cells have no processing attached and output as plain text.

A brief history of Jupyter notebooks

• In 2001, Fernando Perez started development of the iPython project as a way of incorperating prompts and access to previous output, as he continuted development he amalgamated iPython with 2 other projects [^iPy]
• In 2014, Project Jupyter was born out of the initial iPython project. The key aim was to make the project independent of a programming language and allow different code bases to use notebooks. The Name is a reference to the three initial languages:
  • Julia
  • Python
  • R [^Jupyter]
• Jupyter Notebooks and more recently Jupiter Labs are more than just the notebook, they are interactive development environments launched from the command line.
• Jupyter notebooks are used by many online platforms and service providers including: Kaggle, Microsoft Fabric, and the NHS Federated Data Platform.

Pros and cons of using a notebook

thyere are some notable pros and cons to using a Jupyter notebook, it is implortant to be aware of these before selecting them as the right tool for your task.

On the plus side…

• Notebooks are highly interactive and allow cells to be run n any order.
• You can re-run a cell without the whole program, meaning you can iteratively test code segments.
• Notebooks can be used to provide a structured report for an end user regardless of coding knowledge.

Having said that…

• If you are not careful you can save a notebook in a state that cannot run as intended if changes are not checked.
• There are some tasks that are better contained outside of a notebook.

The Toolkit

• You will need the following pre-installed:
  • Language: Python
  • Dependency management: uv
  • Version Control: Git, GitHub Desktop
  • IDE: VS Code (or your preferred IDE)
• You can install all these tools by running the following in PowerShell:
  • winget install astral-sh.uv Microsoft.VisualStudioCode github-desktop

Walkthrough and demonstration

• if reviewing these slids this section is only available in the recording, though the initial steps used should be available on the associated Code Club site page

Resources

• Check out the History of iPython
• You can find out more about Project Jupyter
• The demonstration makes use of this markdown cheatsheet
• Likewise this is the Jupyter shortcuts Cheat Sheet